1. Technical Field
One or more embodiments of the present disclosure relate generally to digital workspaces that allow for viewing, modifying, and curating of analytics data. More specifically, one or more embodiments of the present disclosure relate to systems and methods that dynamically modify a digital analytics workspace to generate a curated digital analytics workspace.
2. Background and Relevant Art
Recent years have seen a proliferation of available digital data for companies regarding their business and customers. Indeed, due to the rise of Internet advertising and sales—together with new and improved digital means of collecting, gathering, and analyzing digital data—businesses can now access a near-constant stream of digital business-related data. For example, businesses now commonly gather, collect, and access digital data regarding customer Internet activity (e.g., clicks, search terms, Internet purchases, time on a site), customer background (e.g., demographics, interests), advertisements (e.g., advertising content, clickthrough rates), web-site activity (e.g., page views, number of visitors), products, revenue, sales, downloads, orders, and other data.
Although such large amounts of data can provide various insights, businesses face new problems in seeking to analyze and utilize such a broad amount (and variety) of constantly changing digital data. Indeed, unlike standard monthly or quarterly reports from the pre-digital age, businesses now have access to volumes of digital information, in a rapidly updated digital stream. Indeed, today many companies hire teams of analysts who have the responsibility of culling and presenting data in a manner that is useful to business decision-makers.
Analysts have become quite proficient at analyzing large volumes of data and presenting the data in a fashion that other individuals in a business can understand. Although common data analytics systems allow analysts to convey information to other members of a business, they introduce their own problems. For instance, companies that utilize traditional systems often become over-reliant on analysts to manipulate and interpret digital data. Indeed, due to the complex nature of common analytics systems, the sheer volume of available data, and the numerous methods available for analyzing the data, many individuals in a business are often overwhelmed and incapable of analyzing information without the assistance of an analyst.
Accordingly, analysts are often over-taxed with data analysis responsibilities. For instance, because other business individuals are unable to manipulate data, analysts are often required to assist at almost every step of a business decision involving data analytics. For example, an analyst may be required where a business simply needs to obtain slightly modified information (e.g., where a decision-maker needs to change the beginning date or end date for the applicable data). Accordingly, analysts can expend a significant amount of time tweaking data in simple ways because other individuals in the business are incapable of doing so.
Common data analytics systems not only over-burden analysts, they tend to under-utilize the skills of employees without data analytics skills. Not surprisingly, the individuals most in-touch with the critical practices, affairs, concerns, and policies of a business are often not the analysts. Yet, because most common data analytics systems require analysts to process digital data at each stage, analysts most frequently make decision regarding what data to utilize, how to process the data, and how to provide the data. Accordingly, businesses utilizing common data analytics features frequently fail to capture the critical ideas and approaches to data-analytics that non-analyst employees can provide.
These and other problems exist with regard to digital analytics workspaces.